ArticleFrontiers in genetics2023
Identification and analysis of hub genes of hypoxia-immunity in type 2 diabetes mellitus.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Integrated Approach for Biomarker Discovery and Mechanistic Insights into the Co-Pathogenesis of Type 2 Diabetes Mellitus and Non-Hodgkin Lymphoma.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The chronic metabolic disease named type 2 diabetes (T2D) accounts for over 90% of diabetes mellitus. An increasing number of evidences have revealed that hypoxia has a significantly suppressive effect on cell-mediated immunity, as well as the utilization of glucose in diabetics. Therefore, we aimed to screen and identify hypoxia-immune-related hub genes in T2D through bioinformatic analysis. The Gene Expression Omnibus (GEO) database was used to get T2D gene expression profile data in the peripheral blood samples (GSE184050), and hypoxia-related genes were acquired from Molecular Signatures Database (MSigDB). Differentially expressed mRNAs (DEGs) and lncRNAs (DELs) between T2D and normal samples were identified by DeSeq2 package. The clusterProfiler package was used to perform enrichment analyses for the overlapped genes of DEGs and hypoxia-related genes. Further, Hypoxia-related hub genes were discovered using two machine learning algorithms. Next, the compositional patterns of immune and stromal cells in T2D and healthy groups were estimated by using xCell algorithm. Moreover, we used the weighted correlation network analysis (WGCNA) to examine the connection between genes and immune cells to screen immune-related genes. Gene Set Enrichment Analysis (GSEA) was used to investigate the functions of the hypoxia-immune-related hub genes. Finally, two peripheral blood cohorts of T2D (GSE184050 and GSE95849) as well as the quantitative real-time PCR (qRT-PCR) experiments for clicinal peripheral blood samples with T2D were used for verification analyses of hub genes. And meanwhile, a lncRNA-TF-mRNA network was constructed. Following the differentially expressed analysis, 38 out of 3822 DEGs were screened as hypoxia-related DEGs, and 493 DELs were found. These hypoxia-related DEGs were mainly enriched in the GO terms of pyruvate metabolic process, cytoplasmic vesicle lumen and monosaccharide binding, and the KEGG pathways of glycolysis/gluconeogenesis, pentose phosphate pathway and biosynthesis of nucleotide sugars. Moreover, 7 out of hypoxia-related DEGs were identified as hub genes. There were six differentially expressed immune cell types between T2D and healthy samples, which were further used as the clinical traits for WGCNA to identify
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.